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Submitted 16 December 2013Accepted 26 February 2014Published 13 March 2014
Corresponding authorLloyd Balbuena,[email protected]
Academic editorTimothy Moss
Additional Information andDeclarations can be found onpage 10
DOI 10.7717/peerj.311
Copyright2014 Balbuena et al.
Distributed underCreative Commons CC-BY 3.0
OPEN ACCESS
Religious attendance after elevateddepressive symptoms: is selection bias atwork?Lloyd Balbuena, Marilyn Baetz and Rudy Bowen
Department of Psychiatry, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
ABSTRACTIn an attempt to determine if selection bias could be a reason that religious atten-dance and depression are related, the predictive value of elevated depressive symp-toms for a decrease in future attendance at religious services was examined in alongitudinal panel of 1,673 Dutch adults. Religious attendance was assessed yearlyover five years using the single question, “how often do you attend religious gatheringsnowadays?” Depressive symptoms were assessed four times within the first year usingthe Depression subscale of the Brief Symptom Inventory. Logistic regression modelsof change in attendance were created, stratifying by baseline attendance status. At-tenders who developed elevated symptoms were less likely to subsequently decreasetheir attendance (relative risk ratio: 0.55, 95% CI [0.38–0.79]) relative to baseline ascompared to those without elevated symptoms. This inverse association remainedsignificant after controlling for health and demographic covariates, and when usingmultiply imputed data to account for attrition. Non-attenders were unlikely to startattending after elevated depressive symptoms. This study provides counter evidenceagainst previous findings that church attenders are a self-selected healthier group.
Subjects Epidemiology, Psychiatry and PsychologyKeywords Selection bias, Religious attendance, Mental health
INTRODUCTIONAttendance at religious services is usually reported to be inversely related to depression
(Baetz et al., 2004; Balbuena, Baetz & Bowen, 2013; Hayward et al., 2012; Maselko, Gilman &
Buka, 2009). However, as with many findings in the literature on religion and depression,
the results have not been consistent, with other studies reporting a null (Ellison & Flannelly,
2009; Miller et al., 2012; Schnittker, 2001) or curvilinear relationship of attendance and
depression (Taylor, Chatters & Nguyen, 2013). This inconsistency has been due in part to
the predominance of cross-sectional designs, which cannot establish causation. We are
aware of only three longitudinal studies examining the relation of depression to subsequent
religious attendance. Recently, it was reported (Maselko et al., 2012) that women having
an onset of depression prior to age 18 were more likely to stop attending religious services
as adults compared to those with adult-onset MDE and those with no lifetime MDE.
Similarly, a longitudinal study (Horowitz & Garber, 2003) reported that depressive episodes
in grades 7–11 predicted lower levels of religious attendance in grade 12. The latter result is
ambiguous because religious attendance in grade 6 also predicted lower odds of depression
How to cite this article Balbuena et al. (2014), Religious attendance after elevated depressive symptoms: is selection bias at work? PeerJ2:e311; DOI 10.7717/peerj.311
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in grades 7–12, although the effect was just shy of statistical significance. Miller and
colleagues (2002) followed a cohort of 146 individuals who had an MDE in childhood
together with a control group of 123 who had no psychiatric disorder over 11 years. At
follow-up, the rates of religious attendance between groups did not differ by childhood
depression status. It is noteworthy that these studies covered the adolescence-to-adult
transition period. Late adolescence is generally a time of profound change. The quest to
establish one’s identity and career (Arnett, 2000) and experimentation with cohabitation,
drugs, and alcohol (Benda & Corwyn, 1997; Thornton, Axinn & Hill, 1992; Uecker, Regnerus
& Vaaler, 2007) could precipitate internal conflicts with religious doctrine, leading to
declines in religious attendance. In short, the developmental processes occurring during
adolescence confounds the relation of depression with religious attendance, in either causal
direction.
Selection effects might work in two ways to confound the relation of depression with
subsequent religious attendance. First, depressed individuals may withdraw from public
worship as part of the overall social disengagement that occurs in depression (Maselko et
al., 2012). Secondly, churchgoers might be less susceptible to depression. A meta-analysis
of 94 studies reported a positive correlation of agreeableness, conscientiousness, and
sociability with religious social investment (Lodi-Smith & Roberts, 2007). In summary,
theory suggests that depressed individuals select themselves out of the churchgoing
demographic while healthy ones select themselves into it. These selection effects, if
operative, would likely overestimate the protective value of religion for mental health.
The protective value of religiosity does have empirical support. Coping with adversity
through religion is well-documented in the literature (Pargament, 1997). “Turning to
God or religion” as a coping strategy has been reported in cancer patients (Bussing,
Ostermann & Koenig, 2007), newly bereaved individuals (Brown et al., 2004), and new
immigrants (Connor, 2009). A study comparing religiosity and spirituality before and
after HIV patients were informed of the diagnosis (Ironson, Stuetzle & Fletcher, 2006)
reported that religiosity (including attendance) increased after the diagnosis became
known. Furthermore, increased religiosity was associated with greater CD4 helper cells
in the blood, indicating slower HIV progression (Ironson, Stuetzle & Fletcher, 2006). A US
longitudinal study reported that depressed individuals were more likely to seek religious
consolation—defined as searching for meaning in problems and difficulties (Ferraro &
Kelley-Moore, 2000). Importantly, even those with no initial religious affiliation sought
religious consolation after depression. This finding seems to support the “no atheists in
foxholes” aphorism. However, adversity or trauma can also cause individuals to turn away
from God or religion (Chen & Koenig, 2006; Fontana & Rosenheck, 2004). We are not aware
of longitudinal studies reporting changes to religious attendance per se (vis-a-vis religiosity
broadly speaking) after a depressive episode aside from the three studies in adolescence
(Horowitz & Garber, 2003; Maselko et al., 2012; Miller et al., 2002) already mentioned.
In this paper, our main objective was to study whether elevated depressive symptoms
introduce selection bias in a longitudinal follow-up of religious attendance. Our research
questions were as follows. First, do religious attenders who develop elevated depressive
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symptoms subsequently decrease their attendance level? Second, do non-attenders who
develop elevated symptoms begin to attend services?
MATERIALS AND METHODSSampleThe data is from the Longitudinal Internet Studies for the Social Sciences (LISS) panel,
a random sample of adults living in the Netherlands (Scherpenzeel, 2011). The panel
of almost 8,000 individuals was drawn from the Dutch-speaking population based
on a list of addresses provided by Statistics Netherlands. The recruitment strategy is
described in more detail elsewhere (Scherpenzeel, 2011). LISS is a continuing study in
which participants complete online questionnaires monthly on topics including family,
economic situation, health, and religion. Each LISS participant has a unique member
ID, so it is possible to combine their responses across different survey modules and from
one wave to another. For the purpose of our study, we merged the religion (n = 7,418) and
mental health (n = 1,804) modules, narrowing our sample to those who responded to both
surveys (n = 1,718) (see Fig. 1A). Waves of religion and depression assessments were not
simultaneous, so hereafter, use the notations Ri and Di to refer to religion and depression
waves, respectively (see Fig. 1B). Our research question requires the following temporal
sequence: R1 → D2 to D4 → R2. By comparing attendance level at R2 with R1, we can test
whether depression predicts a relative decrease at R2. This design requirement forced us to
exclude those individuals (n = 41) who had a different sequence of assessments:
D1 → D2 → R1 → D2 to D4 since for these individuals, one cannot compare attendance
levels before and after depression. Our effective sample size was 1,673.
In the sample, 887 individuals did not attend services and 786 attended services at R1.
(Explained in Measures section below.) The characteristics of these groups are described
in Table 1. After baseline assessment, D1, depression was assessed during a seven-month
span from March to September 2008 while attendance was assessed another four times
in addition to R1, and these assessments occurred on each January from 2009 to 2012.
Our main analytic strategy was to compare the proportion of individuals that changed
their attendance level, stratified by baseline attendance. The change in attendance level
was indexed to R1 attendance since there were no further depression assessments after
September 2008.
MeasuresReligious attendance was assessed with the single question, “Aside from special occasions
such as weddings and funerals, how often do you attend religious gatherings nowadays?” This
was answered on a 7-point Likert scale coded as 1 = everyday, 2 = more than once a week,
3 = once a week, 4 = at least once a month, 5 = only on special religious days, 6 = less often,
and 7 = never. We reverse coded the scale for ease of interpretation—i.e., larger number
indicated higher attendance. Among attenders, we used a dichotomous attendance variable
for decreased (coded 0) and same/increased (coded 1) and a categorical version: decreased,
unchanged, and increased attendance. These categories were formed by subtracting
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Figure 1 Subjects. (A) LISS datasets used and participant flowchart; (B) schedule of religion anddepression assessments.
the R1 attendance level from a given follow-up assessment. For non-attenders in R1, a
dichotomous variable with unchanged (coded 0) and increased (coded 1) was used.
Our substantive predictor was assessed using the Depression subscale of the Brief
Symptom Inventory (Derogatis, 1975). The subscale consists of 6 items rated on a 5-point
scale from “Not at all” to “Extremely”. The BSI is itself a short version of the SCL-90
(Derogatis & Melisaratos, 1983). BSI Depression had an internal reliability ranging from
0.70 to 0.92 in four different studies (Boulet & Boss, 1991; Derogatis & Melisaratos, 1983;
Johnson et al., 2008; Kellett et al., 2003) and correlated with the Beck Depression Inventory,
r = .71 and r = .77, in two other studies (Stukenberg, Dura & Kiecolt-Glaser, 1990; Prinz
et al., 2013). Two studies validated BSI Depression against either DSM-III or DSM-IV-TR
major depression (Stukenberg, Dura & Kiecolt-Glaser, 1990; Johnson et al., 2008). ROC
analyses reported area under the curve as 0.83 in community dwelling older adults, but a
more modest 0.65 among psychiatric inpatients.
For copyright reasons, the LISS dataset only provided the normative category of each
respondent and not the responses to individual questions. These normative categories of
BSI depression were: 1 = very low, 2 = low, 3 = below average, 4 = average, and 5 = above
average, 6 = high, 7 = very high. We dichotomized them into low (very low to above
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Table 1 Comparison of attenders and non-attenders. Characteristics of Dutch individuals who responded to mental health (Wave 1) and religiousattendance (Wave 1–Jan 2008) of the Longitudinal Internet Studies for the Social Sciences (LISS), Netherlands (n = 1,673).
Service attenders at R1 Non-attenders of service at R1 p of the difference
n 786 887
Mean age (sd) 49.60 (18.22) 45.90 (17.21)
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attenders, for each religion follow-up year. Chi-square tests of association were performed.
To examine change longitudinally and control for confounders, we created a multinomial
logistic regression model among attenders. Relative risk ratios of decreasing or increasing
attendance over maintaining attendance were calculated.
To examine if non-attenders start attending services after elevated depression, a
binary logistic regression model was created with change in attendance (0 = no change;
1 = attended) as dependent variable. We entered the covariates in all regression models
to control for confounding and robust standard errors were calculated to account for
correlated errors in repeated observations.
Missing dataMissing values for the covariates at baseline did not differ between attenders and non-
attenders. To examine whether our results would be biased by differential attrition between
attenders and non-attenders, we performed multiple imputation on our dependent and
predictor variables using a set of 15 socio-demographic and psychological variables
assessed at baseline. (These variables are available from the authors upon request.) The
imputation procedure was implemented using REALCOM-IMPUTE (Carpenter, Goldstein
& Kenward, 2011) software and 10 imputed datasets were generated, which were then
analyzed using Stata 12.1. Our regression models were then repeated using the multiply
imputed data.
RESULTSReligious attenders were more likely to be female, older, and married as compared
to non-attenders. Attenders and non-attenders were similar in self-rated health, in
the proportion having chronic conditions, and in the distribution of BSI Depression
normative scores. From March to September 2008, 491 individuals developed elevated
depression. The chi-square tests in each of the four follow-up waves showed no association
between depression status and subsequent decrease in attendance with one exception.
In 2012, those with elevated BSI Depression among baseline attenders were more likely
to keep or increase their 2008 attendance levels. In post-hoc analysis, we repeated the
change in attendance analysis by depression category stratified first by gender and then by
age category. In gender-stratified analysis, no associations were found in each attendance
follow-up. In age-stratified analysis, no associations were found in the first two years after
depression assessments took place. In both R3 and R4, those 48 years old and above and
with elevated depression were more likely to maintain or increase attendance levels (both
p’s < .05). (See Table 2. The tables stratified by gender or age category are available from the
authors by request.)
In multinomial logistic regression modeling, elevated symptoms predicted lower
probability of a decrease in attendance (relative risk ratio = 0.55, 95% CI [0.38–0.79])
relative to baseline attendance levels among attenders (see Table 3 and Fig. 2). Among
non-attenders, elevated symptoms were unrelated to a subsequent increase in attendance
with one exception. The estimates were not materially different when the models were run
using multiply imputed data.
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Table 2 Change in attendance by depression status. BSI depression vs changes in attendance in the Longitudinal Internet Studies for the SocialSciences (LISS), Netherlands.
Service attenders at R1 Non-attenders of service at R1
2009 attendance vs2008
Unelevated BSIdepression n (%)
Elevated BSIdepression n (%)
χ2 p 2009 attendance vs2008
Unelevated BSIdepression n (%)
Elevated BSIdepression n (%)
χ2 p
Same or increase 371 (73) 184 (78) 2.31 .13 Same 394 (67) 177 (69) .47 .49
Decrease 139 (27) 52 (22) Increase 194 (33) 78 (31)
2010 attendance vs2008
Unelevated BSIdepression n (%)
Elevated BSIdepression n (%)
χ2 p 2010 attendance vs2008
Unelevated BSIdepression n (%)
Elevated BSIdepression n (%)
χ2 p
Same or increase 392 (77) 191 (81) 1.56 .21 Same 348 (59) 154 (60) .11 .74
Decrease 118 (23) 45 (19) Increase 240 (41) 101 (40)
2011 attendance vs2008
Unelevated BSIdepression n (%)
Elevated BSIdepression n (%)
χ2 p 2011 attendance vs2008
Unelevated BSIdepression n (%)
Elevated BSIdepression n (%)
χ2 p
Same or increase 390 (76) 189 (80) 1.22 .27 Same 315 (54) 142 (56) .32 .57
Decrease 120 (24) 47 (20) Increase 273 (46) 113 (44)
2012 attendance vs2008
Unelevated BSIdepression n (%)
Elevated BSIdepression n (%)
χ2 p 2012 attendance vs2008
Unelevated BSIdepression n (%)
Elevated BSIdepression n (%)
χ2 p
Same or increase 367 (72) 189 (80) 5.61 .02 Same 291 (49) 136 (53) 1.05 .31
Decrease 143 (28) 47 (20) Increase 297 (50) 119 (47)
Notes.If the individual had a norm-referenced score of “high” or “very high” in any of the three assessments in 2008, the individual was assigned to elevated and otherwise tounelevated.BSI, Brief Symptom Inventory.
Table 3 Regression models. Logistic models of change in religious attendance levels over 5 years from the Longitudinal Internet Studies for theSocial Sciences (LISS), Netherlands.
Religious attenders at baseline Non-attenders at baseline
Model 1a:complete cases
Model 1Aa:multiply imputed data(10 imputations)
Model 2a:complete cases
Model 2Aa:multiply imputed data(10 imputations)
Relative risk ratio(95% CI)
Relative risk ratio(95% CI)
Odds ratio(95% CI)
Odds ratio(95% CI)
Outcome: decreased attendance
Predictor: elevated depression 0.55 (0.38–0.79)** 0.58 (0.41–0.83)** N/A N/A
Outcome: same attendance (Reference category) (Reference category)
Outcome: increased attendance
Predictor: elevated depression 0.90 (0.68–1.19) 1.27 (0.78–2.07) 0.93 (0.73–1.18) 1.10 (0.55–2.21)
Notes.a In these models, the following covariates have been controlled: existing chronic condition, gender, marital status, income, and age.
** p values: .01.
DISCUSSIONThe main finding in this study is that elevated BSI depression does not cause a subsequent
decrease in attendance. On the contrary, elevated BSI depression predicts same or
increased levels of attendance as compared with prior to elevated BSI depression. None
of our results across three analytical strategies showed a decrease in attendance. Although
unstratified analyses did not yield a significant association, gender and age-stratified results
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Figure 2 Probabilities. Predicted probabilities of a decrease, same, and increase in religious attendance(95% CI).
indicated an association of continued or increased attendance with elevated symptoms.
We do not interpret this association because other variables could possibly explain the
association. The multinomial logistic model among attenders, with covariates controlled
for, indicated that elevated symptoms predicted same or increased attendance. As a
secondary finding, non-attenders were not likely to seek recourse in religion by starting
to attend services.
Our main finding addresses the issue of selection bias in studies of religious attendance
and mental health. To recap, when depressed individuals stop attending services, the
apparent protection afforded by attendance would be inflated. This selection bias would
be most pronounced in cross-sectional studies. In longitudinal epidemiologic studies, bias
remains an issue because there might be differential attrition in attendance by depression
status. Since we found that attenders who develop elevated symptoms become less likely
to drop out of worship, then they might in effect be overrepresented among churchgoers.
Therefore, the protective effect of religious attendance might in fact be underestimated,
contrary to the reverse causation argument.
Religious individuals turn to their faith in times of distress as a means of coping
(Brown et al., 2004; Bussing, Ostermann & Koenig, 2007; Ironson, Stuetzle & Fletcher,
2006). Religious worship is a source of social support and allows individuals to cope
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with life-threatening conditions by providing “medicine for the spirit” (Cummings
& Pargament, 2010). A longitudinal study (Li & Ferraro, 2005) on depression and
volunteering among older adults reported that elevated depression at wave 2 predicted
seeking out volunteering opportunities at wave 3. This was explained as a compensatory
mechanism to alleviate negative affect, via social integration. These older adults had
physical health problems in addition to depression and the physical health problems were
the operative barriers. Consistent with this finding, Chen & Koenig (2006) reported that
in elderly, medically ill individuals, an increased severity of physical illness predicted a
decrease in organizational religiousness. Importantly, the association was completely
mediated by physical activity limitations. Together with our own results, these suggest
that it is not depression per se that causes a disengagement from participation in religious
service. In effect, reverse causation and non-random attrition might be more relevant to
studies examining religious attendance and physical health.
The null association of elevated depression with subsequent attendance among
non-attenders indicates that non-attenders do not start attending in the face of adversity.
By contrast, Ferraro & Kelley-Moore (2000) reported that depression, cancer, and chronic
conditions lead to consolation-seeking even among those with no religious affiliation.
The two points of view can be reconciled because attendance is not necessary for
consolation-seeking. The need to find meaning in illness is shared by believers and
non-believers who may turn to nature, arts, music, and relationships (Burnard, 1988).
An intriguing study (Farias et al., 2013) reported that rowers about to enter a
competition reported greater belief in science as compared with those in a training
session. The authors concluded that affirming one’s secular worldview serves a similar
function as religious faith in moments of stress or existential anxiety. Hence, affiliated
individuals cope by means of religion while non-attending ones seek support and meaning
elsewhere. It appears that depressive symptoms are not a strong driver of entry into or exit
from the churchgoing demographic. More empirical research is needed to validate this
interpretation.
Our study has several important limitations. First, we relied on the depression subscale
of the BSI to assess depressive symptoms. It has been argued that although the global BSI
scale is a valid measure of distress, it is not intended to screen for particular psychiatric
disorders (Asner-Self, Schreiber & Marotta, 2006). We set the BSI cut-off for depression
at “high” or “very high” but there is no way to tell whether this is in concordance with
clinically diagnosed depression. We therefore allow for the possibility that the depressed
group that we studied might differ from a group with formally diagnosed major depression
(Sareen et al., 2013). Secondly, there were no assessments of depression from 2009 to
2012—the period during which religious attendance was compared to initial attendance.
As such, all religious attendance changes were indexed to depression levels in 2008 only. We
cannot rule out the possibility that attendance levels after 2008 were related to unmeasured
elevated symptoms after 2008. Third, the sample that we worked with was restricted to
LISS participants who responded to both religion and mental health modules and might
not be representative of the Dutch national population. Fourth, while we have ruled out
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sampling bias in terms of attrition in attendance, we could not address whether personality
traits such as greater resilience to depression characterized attenders in the first place.
Finally, other health-related reasons, including physical disability, might be correlated with
depression and could cause selection bias. This is beyond the scope of our study. The major
strength of this study was that depression assessments were proximal to those of religion.
Our sample also consisted of a small fraction (5%) of late adolescents and is less affected by
the well-reported decline in religious attendance in adolescence.
Within the limitations of our study, we conclude that elevated depressive symptoms do
not cause religiously affiliated individuals to subsequently decrease attendance at religious
services.
ACKNOWLEDGEMENTSThe authors are grateful to the CentERdata at Tilburg University in the Netherlands for
making the data available for use.
ADDITIONAL INFORMATION AND DECLARATIONS
FundingThe authors received no funding for this work.
Competing InterestsThe authors have no financial or competing interests to declare.
Author Contributions• Lloyd Balbuena conceived and designed the experiments, analyzed the data, wrote the
paper, prepared figures and/or tables.
• Marilyn Baetz and Rudy Bowen conceived and designed the experiments, wrote the
paper, reviewed drafts of the paper.
REFERENCESArnett JJ. 2000. Emerging adulthood—a theory of development from the late teens through the
twenties. American Psychologist 55:469–480 DOI 10.1037/0003-066X.55.5.469.
Asner-Self KK, Schreiber JB, Marotta SA. 2006. A cross-cultural analysis of the BriefSymptom Inventory-18. Cultural Diversity & Ethnic Minority Psychology 12:367–375DOI 10.1037/1099-9809.12.2.367.
Baetz M, Griffin R, Bowen R, Koenig HG, Marcoux E. 2004. The association between spiritual andreligious involvement and depressive symptoms in a Canadian population. Journal of Nervousand Mental Disease 192:818–822 DOI 10.1097/01.nmd.0000146735.73827.85.
Balbuena L, Baetz M, Bowen R. 2013. Religious attendance, spirituality, and major depression inCanada: a 14-year follow-up study. Canadian Journal of Psychiatry 58:225–232.
Benda BB, Corwyn RF. 1997. A test of a model with reciprocal effects between religiosity andvarious forms of delinquency using 2-stage least squares regression. Journal of Social ServiceResearch 22:27–52 DOI 10.1300/J079v22n03 02.
Balbuena et al. (2014), PeerJ, DOI 10.7717/peerj.311 10/12
https://peerj.comhttp://dx.doi.org/10.1037/0003-066X.55.5.469http://dx.doi.org/10.1037/1099-9809.12.2.367http://dx.doi.org/10.1097/01.nmd.0000146735.73827.85http://dx.doi.org/10.1300/J079v22n03_02http://dx.doi.org/10.7717/peerj.311
-
Bijl RV, Ravelli A, van Zessen G. 1998. Prevalence of psychiatric disorder in the generalpopulation: results of the Netherlands Mental Health Survey and Incidence Study (NEMESIS).Social Psychiatry and Psychiatric Epidemiology 33:587–595 DOI 10.1007/s001270050098.
Boulet J, Boss M. 1991. Reliability and validity of the Brief Symptom Inventory. PsychologicalAssessment: A Journal of Consulting & Clinical Psychology 3:433–437DOI 10.1037/1040-3590.3.3.433.
Brown SL, Nesse RM, House JS, Utz RL. 2004. Religion and emotional compensation: results froma prospective study of widowhood. Personality and Social Psychology Bulletin 30:1165–1174DOI 10.1177/0146167204263752.
Burnard P. 1988. The spiritual needs of atheists and agnostics. Professional Nurse 4:130–132.
Bussing A, Ostermann T, Koenig HG. 2007. Relevance of religion and spirituality in Germanpatients with chronic diseases. International Journal of Psychiatry in Medicine 37:39–57DOI 10.2190/60W7-1661-2623-6042.
Carpenter JR, Goldstein H, Kenward MG. 2011. REALCOM-IMPUTE software for multilevelmultiple imputation with mixed response types. Journal of Statistical Software 45:1–14.
Chen YY, Koenig HG. 2006. Do people turn to religion in times of stress?: an examination ofchange in religiousness among elderly, medically ill patients. Journal of Nervous and MentalDisease 194:114–120 DOI 10.1097/01.nmd.0000198143.63662.fb.
Connor P. 2009. Immigrant religiosity in Canada: multiple trajectories. International Migration &Integration 10:159–175 DOI 10.1007/s12134-009-0097-9.
Cummings JP, Pargament KI. 2010. Medicine for the spirit: religious coping in individuals withmedical conditions. Religions 1:28–53 DOI 10.3390/rel1010028.
Derogatis LR. 1975. Brief Symptom Inventory. Baltimore: Clinical Psychometric Research.
Derogatis LR, Melisaratos N. 1983. The Brief Symptom Inventory—an introductory report.Psychological Medicine 13:595–605 DOI 10.1017/S0033291700048017.
Ellison CG, Flannelly KJ. 2009. Religious involvement and risk of major depression in aprospective nationwide study of African American adults. Journal of Nervous and Mental Disease197:568–573 DOI 10.1097/NMD.0b013e3181b08f45.
Farias M, Newheiser AK, Kahane G, de Toledo Z. 2013. Scientific faith: belief in scienceincreases in the face of stress and existential anxiety. Journal of Experimental Social Psychology49:1210–1213 DOI 10.1016/j.jesp.2013.05.008.
Ferraro KF, Kelley-Moore JA. 2000. Religious consolation among men and women: dohealth problems spur seeking? Journal for the Scientific Study of Religion 39:220–234DOI 10.1111/0021-8294.00017.
Fontana A, Rosenheck R. 2004. Trauma, change in strength of religious faith, and mentalhealth service use among veterans treated for PTSD. Journal of Nervous and Mental Disease192:579–584 DOI 10.1097/01.nmd.0000138224.17375.55.
Hayward RD, Owen AD, Koenig HG, Steffens DC, Payne ME. 2012. Religion and the presenceand severity of depression in older adults. American Journal of Geriatric Psychiatry 20:188–192DOI 10.1097/JGP.0b013e31822ccd51.
Horowitz JL, Garber J. 2003. Relation of intelligence and religiosity to depressive disorders inoffspring of depressed and nondepressed mothers. Journal of the American Academy of Childand Adolescent Psychiatry 42:578–586 DOI 10.1097/01.CHI.0000046831.09750.03.
Ironson G, Stuetzle R, Fletcher MA. 2006. An increase in religiousness/spirituality occurs afterHIV diagnosis and predicts slower disease progression over 4 years in people with HIV. Journalof General Internal Medicine 21(Suppl 5):S62–S68 DOI 10.1111/j.1525-1497.2006.00648.x.
Balbuena et al. (2014), PeerJ, DOI 10.7717/peerj.311 11/12
https://peerj.comhttp://dx.doi.org/10.1007/s001270050098http://dx.doi.org/10.1037/1040-3590.3.3.433http://dx.doi.org/10.1177/0146167204263752http://dx.doi.org/10.2190/60W7-1661-2623-6042http://dx.doi.org/10.1097/01.nmd.0000198143.63662.fbhttp://dx.doi.org/10.1007/s12134-009-0097-9http://dx.doi.org/10.3390/rel1010028http://dx.doi.org/10.1017/S0033291700048017http://dx.doi.org/10.1097/NMD.0b013e3181b08f45http://dx.doi.org/10.1016/j.jesp.2013.05.008http://dx.doi.org/10.1111/0021-8294.00017http://dx.doi.org/10.1097/01.nmd.0000138224.17375.55http://dx.doi.org/10.1097/JGP.0b013e31822ccd51http://dx.doi.org/10.1097/01.CHI.0000046831.09750.03http://dx.doi.org/10.1111/j.1525-1497.2006.00648.xhttp://dx.doi.org/10.7717/peerj.311
-
Johnson ME, Chipp CL, Brems C, Neal DB. 2008. Receiver operating characteristics for the briefsymptom inventory depression, paranoid ideation, and psychoticism scales in a large sample ofclinical inpatients. Psychological Reports 102:695–705 DOI 10.2466/pr0.102.3.695-705.
Kellett S, Beail N, Newman DW, Frankish P. 2003. Utility of the brief symptom inventory inthe assessment of psychological distress. Journal of Applied Research in Intellectual Disabilities16:127–134 DOI 10.1046/j.1468-3148.2003.00152.x.
Li YQ, Ferraro KF. 2005. Volunteering and depression in later life: social benefit or selectionprocesses? Journal of Health and Social Behavior 46:68–84 DOI 10.1177/002214650504600106.
Lodi-Smith J, Roberts BW. 2007. Social investment and personality: a meta-analysis of therelationship of personality traits to investment in work, family, religion, and volunteerism.Personality and Social Psychology Review 11:68–86 DOI 10.1177/1088868306294590.
Maselko J, Gilman SE, Buka S. 2009. Religious service attendance and spiritual well-being aredifferentially associated with risk of major depression. Psychological Medicine 39:1009–1017DOI 10.1017/S0033291708004418.
Maselko J, Hayward RD, Hanlon A, Buka S, Meador K. 2012. Religious service attendance andmajor depression: a case of reverse causality? American Journal of Epidemiology 175:576–583DOI 10.1093/aje/kwr349.
Miller L, Weissman M, Gur M, Greenwald S. 2002. Adult religiousness and history of childhooddepression: eleven-year follow-up study. Journal of Nervous and Mental Disease 190:86–93DOI 10.1097/00005053-200202000-00004.
Miller L, Wickramaratne P, Gameroff MJ, Sage M, Tenke CE, Weissman MM. 2012. Religiosityand major depression in adults at high risk: a ten-year prospective study. American Journal ofPsychiatry 169:89–94 DOI 10.1176/appi.ajp.2011.10121823.
Pargament KI. 1997. The psychology of religion and coping: theory, research, practice. New York,London: Guilford Press.
Prinz U, Nutzinger DO, Schulz H, Petermann F, Braukhaus C, Andreas S. 2013. Comparativepsychometric analyses of the SCL-90-R and its short versions in patients with affective disorders.BMC Psychiatry 13:104 DOI 10.1186/1471-244X-13-104.
Sareen J, Henriksen CA, Stein MB, Afifi TO, Lix LM, Enns MW. 2013. Common mental disorderdiagnosis and need for treatment are not the same: findings from a population-basedlongitudinal survey. Psychological Medicine 43:1941–1951 DOI 10.1017/S003329171200284X.
Scherpenzeel A. 2011. Data collection in a probability-based internet panel: how the LISSpanel was built and how it can be used. Bulletin of Sociological Methodology 109:56–61DOI 10.1177/0759106310387713.
Schnittker J. 2001. When is faith enough? The effects of religious involvement on depression.Journal for the Scientific Study of Religion 40:393–411 DOI 10.1111/0021-8294.00065.
Stukenberg KW, Dura JR, Kiecolt-Glaser JK. 1990. Depression screening scale validation in anelderly, community-dwelling population. Psychological Assessment: A Journal of Consulting &Clinical Psychology 2:134–138 DOI 10.1037/1040-3590.2.2.134.
Taylor RJ, Chatters LM, Nguyen AW. 2013. Religious participation and DSM IV major depressivedisorder among Black Caribbeans in the United States. Journal of Immigrant and MinorityHealth 15:903–909 DOI 10.1007/s10903-012-9693-4.
Thornton A, Axinn WG, Hill DH. 1992. Reciprocal effects of religiosity, cohabitation, andmarriage. American Journal of Sociology 98:628–651 DOI 10.1086/230051.
Uecker JE, Regnerus MD, Vaaler ML. 2007. Losing my religion: the social sources of religiousdecline in early adulthood. Social Forces 85:1667–1692 DOI 10.1353/sof.2007.0083.
Balbuena et al. (2014), PeerJ, DOI 10.7717/peerj.311 12/12
https://peerj.comhttp://dx.doi.org/10.2466/pr0.102.3.695-705http://dx.doi.org/10.1046/j.1468-3148.2003.00152.xhttp://dx.doi.org/10.1177/002214650504600106http://dx.doi.org/10.1177/1088868306294590http://dx.doi.org/10.1017/S0033291708004418http://dx.doi.org/10.1093/aje/kwr349http://dx.doi.org/10.1097/00005053-200202000-00004http://dx.doi.org/10.1176/appi.ajp.2011.10121823http://dx.doi.org/10.1186/1471-244X-13-104http://dx.doi.org/10.1017/S003329171200284Xhttp://dx.doi.org/10.1177/0759106310387713http://dx.doi.org/10.1111/0021-8294.00065http://dx.doi.org/10.1037/1040-3590.2.2.134http://dx.doi.org/10.1007/s10903-012-9693-4http://dx.doi.org/10.1086/230051http://dx.doi.org/10.1353/sof.2007.0083http://dx.doi.org/10.7717/peerj.311
Religious attendance after elevated depressive symptoms: is selection bias at work?IntroductionMaterials and MethodsSampleMeasuresCovariatesStatistical analysisMissing data
ResultsDiscussionAcknowledgementsReferences